xcmsVis

Modern, interactive visualizations for XCMS metabolomics data using ggplot2.

Overview

xcmsVis provides ggplot2 implementations of XCMS plotting functions, enabling:

• Modern, publication-ready visualizations
• Interactive plots through plotly integration
• Consistent styling across all plots
• Full compatibility with both XCMSnExp and XcmsExperiment objects

This package complements the XCMS package by reimplementing its base graphics plots using ggplot2.

Installation

# Install from GitHub (development version)
# devtools::install_github("yourusername/xcmsVis")

Features

Current Functions

• gplotAdjustedRtime() - ggplot2 version of plotAdjustedRtime()
• gplotChromPeaks() - ggplot2 version of plotChromPeaks()
• gplotChromPeakImage() - ggplot2 version of plotChromPeakImage()
• gplot() - ggplot2 version of plot() for XChromatogram objects
• ghighlightChromPeaks() - ggplot2 version of highlightChromPeaks()

Planned Functions

Future releases will include ggplot2 versions of:

• plotQC()
• plotChromPeakDensity()
• And more!

Usage

library(xcmsVis)
library(xcms)

# Works with XcmsExperiment (XCMS v4+)
xdata <- readMsExperiment(files = mzml_files)
# ... perform peak detection, alignment, etc.

# Create retention time adjustment plot
p <- gplotAdjustedRtime(xdata, color_by = sample_group)
print(p)

# Make it interactive
library(plotly)
ggplotly(p, tooltip = "text")

# Also works with XCMSnExp (XCMS v3)
xdata_v3 <- readMSData(files = mzml_files, mode = "onDisk")
# ... perform XCMS workflow
p <- gplotAdjustedRtime(xdata_v3, color_by = sample_group)

Motivation

This package addresses the need discussed in XCMS issue #551 for ggplot2-based visualizations that can be easily made interactive using plotly.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details